{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercício 3 - Degree Distribution\n",
    "\n",
    "Ler e comparar a distribuição dos graus dos vérticeis de dois datasets: um deles é a versão mais completa do dataset do facebook usado nos exercícios 1 e 2, com mais vértices, e outro é um dataset randômico. Os dois têm a mesma quantidades de vértices e arestas.\n",
    "\n",
    "Procedimento:\n",
    "- Ler o dataset __\"facebook_combined.txt\"__, que está em formato csv, separado por espaço, sem cabeçalho.\n",
    "- Ler o dataset __\"another_random_graph.net\"__, que está em formato pajek (a função [__networkx.read_pajek__](http://networkx.readthedocs.io/en/networkx-1.11/reference/generated/networkx.readwrite.pajek.read_pajek.html) pode ajudá-lo)\n",
    "- Computar o degree dos dois grafos (a função [__networkx.degree__](http://networkx.readthedocs.io/en/networkx-1.11/reference/generated/networkx.classes.function.degree.html) pode ajudá-lo)\n",
    "- Usar a função __plot_degree_graph__, definida logo abaixo, para gerar o gráfico de distribuição dos graus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "def plot_degree_graph(degree_dict, title=''):\n",
    "    degree = sorted( Counter( degree_dict.values() ).iteritems(), key=lambda (k, v): k )\n",
    "    plt.clf()\n",
    "    plt.close()\n",
    "    plt.title(title)\n",
    "    plt.plot([ k for k, __ in degree ], \n",
    "             [ v for __, v in degree ])\n",
    "    plt.xlabel('amount of nodes')\n",
    "    plt.ylabel('degree')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEZCAYAAABvpam5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nopMQEMBcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b8412a590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b840ba190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"facebook_combined.txt\", sep=\" \", header = None)\n",
    "G = nx.Graph()\n",
    "G.add_edges_from( df.values )\n",
    "plot_degree_graph(nx.degree(G), 'Facebook Degree Centrality')\n",
    "\n",
    "rG = nx.read_pajek(\"another_random_graph.net\")\n",
    "plot_degree_graph(nx.degree(rG), 'Random Degree Centrality')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
